Natural Language Processing uses Artificial Intelligence to process text to “understand” the “meaning” (which we call “intent”) behind the text of a person’s “utterance”. We can also analyse the text for things like how a person “feels” about the topic, using things like “sentiment analysis”
Data Lakes are basically big data stores (like a database) that can take almost any kind of data from the enterprise and put it all together in one big “pool” of data. Then this pool can be queried like a regular database. We can also run predictive analytics Artificial Intelligence on it, in order to ask “what if” questions like – if I change this product, how does that affect sales forcast? or if I change this resource, how does that affect staffing?
TA Document Engine is created when an AI is “trained” to understand a “type” of a document. We teach an AI engine what a type of document “looks” like in your specific business and context, and how to “extract” needed and specific types of information from these kinds of documents. When new documents of that kind are added to the repository, the system can automatically “index” them for a full, cognitive search.
These kinds of chatbots have questions with pre-selected answers in the form of buttons or “canned” selections the user pics from – while these basic interactions don’t use the mathematics and cloud resources of the more complex AI algorithms, they do allow complex system and process interactions in a very easy to use and understand user interface
We offer special interfaces to our chatbots (through sub-contractors) via smart speakers, phones, etc. These allow users to “speak” their requests and have the bot “understand” their speech. This is done by an Artificial Intelligence system that processes the speech in the application then turns it over the Natural Language Processing system (see above) for processing the actual workflow
We talk a lot about “cognitive” things in AI. What we mean by that is that the “context” of a question is as important as the question itself. A regular search box uses keywords to find its results. A cognitive search uses EVERYTHING the user enters to try to find the most relevant answers. Cognitive search can be applied across multiple types of data and data stores simultaneously.
As an example, in a normal search engine, you could search for ” sales data Q3 2021″ and if your IT department was really good, you might get what you need. In a cognitive search, you could ask “send all sales figures in the documents for Q3 for the last 5 years to my desk” and have those specific paragraphs from the data sent to a desk.